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RESPONSE TO COMMENTARIES: MOVING TOWARDS AN EVIDENCE‐BASED POLICY AROUND CANNABIS USE

2010· letter· en· W1492933312 on OpenAlexaff
John Macleod, Matthew Hickman

Bibliographic record

VenueAddiction · 2010
Typeletter
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsInstitute of Infection and Immunity
FundersEconomic and Social Research Council
KeywordsCannabisContext (archaeology)HarmPsychiatryPsychologyCriticismScientific evidencePoliticsEvidence-based policyCriminologyMedicinePolitical sciencePositive economicsSocial psychologyLawEpistemologyAlternative medicineHistoryPhilosophyEconomics

Abstract

fetched live from OpenAlex

We are glad that our invited commentary [1] on the relationship between cannabis evidence and cannabis policy has stimulated debate, and we are grateful to our scientific colleagues for their thoughtful responses [2–6]. These responses raise more issues than we can address adequately here, so we will stick to the main points. Our paper had two aims. The first was to examine, and attempt to understand, the recent scientific debate around possible cannabis harms. The second was to discuss what a policy around cannabis based on this evidence might look like. We recognize that political support for evidence-based policy in this context may be, at best, rhetorical. Some of our commentators criticize our focus on evidence around cannabis and psychosis. This seems a little unfair because, for the past decade, and not just in the United Kingdom, this possible harm of cannabis use has driven the policy debate [7]. David Fergusson [2] was unhappy with our distinction between evidence on psychotic symptoms and evidence on schizophrenia. Again, this criticism seems misguided—we did not ignore the former, but the fact that we accorded a different status to the latter is simply normal epidemiological practice. In the same way, a cardiovascular epidemiologist would accord a different status to associations between stress and chest pain compared to objective evidence of coronary vascular disease [8,9]. Professor Fergusson [2] has also misunderstood our critique of the cannabis psychosis/schizophrenia evidence if he thinks it rests on ‘increasingly elaborate’ arguments. It rests now, and always has, on a very simple argument. Apparent independent effects of cannabis use on risk of psychosis may be due to residual confounding and measurement error [10]. That is not to say they are not causal—they might be, but it is simply impossible to know. Most scientists, including our commentators, agree on this. All we appear to be arguing about is the level of uncertainty. What about other possible harms? Cannabis use has been associated with several adverse outcomes, as listed by Professor Wittchen [3], although as we have discussed elsewhere, in relation to most of these the strength of the evidence that the association has a causal basis is weaker than in the case of psychosis [11]. We agree with Hall & Degenhardt [4] that cannabis dependence can be added safely to the list of cannabis harms and apologize if we appeared to downplay the importance of this. Naively, we thought that another point we could all agree upon would be the harm that cannabis causes, through concomitant tobacco use. Can we be very clear that our assertions around tobacco and cannabis bear absolutely no relation to any pet thesis we are trying to promote and are not influenced by any ‘wish bias’? Perhaps we are hoist by our own petard here: guilty of over-interpreting the limited empirical evidence that a substantial proportion of cannabis users smoke cannabis mixed with tobacco, and that for many of them their cannabis use reinforces their tobacco use [12–14]. Obviously, if in most of the cannabis-smoking world, cannabis is not smoked with tobacco then our assertions in this regard are unlikely to be true. However, even if we cannot agree on the precise hierarchical structure of a list of possible cannabis harms we seem to have come to a point where we can agree on the list and the fact that, based on the precautionary principle, we have a basis to advocate prevention. The question then is how do we pursue this goal? We believe that any policy be judged on the simple criteria used commonly to guide decisions around public health interventions: are they cost-effective, do they cause more good than harm and are they acceptable to people at whom they are aimed? Robert MacCoun [5] found our suggestion that cannabis prohibition probably failed this test ‘awfully brash’. This surprised us; all our respondents who commented on the issue agreed that there is no strong evidence that prohibition reduces cannabis use. Alongside this lack of evidence of benefit, prohibition also incurs considerable costs [15,16]. As MacCoun [15] suggests pessimistically, the alternatives could always be worse. This is true, and is the reason why the alternatives should be evaluated rigorously. None.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.061
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.057
GPT teacher head0.357
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2010
Admission routes1
Has abstractyes

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